1. Field of the Invention
This invention relates to an optimization control method for a shock absorber having a non-linear kinetic characteristic.
2. Description of the Related Art
Feedback control systems are widely used to maintain the output of a dynamic system at a desired value in spite of external disturbance forces that would move the output away from the desired value. For example, a household furnace controlled by a thermostat is an example of a feedback control system. The thermostat continuously measures the air temperature of the house, and when the temperature falls below a desired minimum temperature, the thermostat turns the furnace on. When the furnace has warmed the air above the desired minimum temperature, then the thermostat turns the furnace off. The thermostat-furnace system maintains the household temperature at a constant value in spite of external disturbances such as a drop in the outside air temperature. Similar types of feedback control are used in many applications.
A central component in a feedback control system is a controlled object, otherwise known as a process xe2x80x9cplant,xe2x80x9d whose output variable is to be controlled. In the above example, the plant is the house, the output variable is the air temperature of the house, and the disturbance is the flow of heat through the walls of the house. The plant is controlled by a control system. In the above example, the control system is the thermostat in combination with the furnace. The thermostat-furnace system uses simple on-off feedback control to maintain the temperature of the house. In many control environments, such as motor shaft position or motor speed control systems, simple on-off feedback control is insufficient. More advanced control systems rely on combinations of proportional feedback control, integral feedback control, and derivative feedback control. Feedback that is the sum of proportional plus integral plus derivative feedback is often referred to as PID control.
The PID control system is a linear control system that is based on a dynamic model of the plant. In classical control systems, a linear dynamic model is obtained in the form of dynamic equations, usually ordinary differential equations. The plant is assumed to be relatively linear, time invariant, and stable. However, many real-world plants are time varying, highly nonlinear, and unstable. For example, the dynamic model may contain parameters (e.g., masses, inductances, aerodynamic coefficients, etc.) which are either poorly known or depend on a changing environment. Under these conditions, a linear PID controller is insufficient.
Evaluating the motion characteristics of a nonlinear plant is often difficult, in part due to the lack of a general analysis method. Conventionally, when controlling a plant with nonlinear motion characteristics, it is common to find certain equilibrium points of the plant and the motion characteristics of the plant are linearized in a vicinity near an equilibrium point. Control is then based on evaluating the pseudo (linearized) motion characteristics near the equilibrium point. This technique works poorly, if at all, for plants described by models that are unstable or dissipative. The optimization control for a non-linear kinetic characteristic of a controlled process has not been well developed. A general analysis method for non-linear kinetic characteristic has not been previously available, so a control device suited for the linear-kinetic characteristic is often substituted. Namely, for the controlled process with the non-linear kinetic characteristic, a suitable balance point for the kinetic characteristic is picked. Then, the kinetic characteristic of the controlled process is linearized in a vicinity of the balance point, whereby the evaluation is conducted relative to pseudo-kinetic characteristics.
However, this method has several disadvantageous. Although the optimization control may be accurately conducted around the balance point, its accuracy decreases beyond this balance point. Further, this method cannot typically keep up with various kinds of environmental changes around the controlled process.
Shock absorbers used for automobiles and motor cycles are one example of a controlled process having the non-linear kinetic characteristic. The optimization of the non-linear kinetic characteristic has been long sought because vehicle""s turning performances and ride are greatly affected by the damping characteristic and output of the shock absorbers.
The present invention solves these and other problems by providing a new control system for optimizing a shock absorber having a non-linear kinetic characteristic. The new AI control system uses a fitness (performance) function that is based on the physical laws of minimum entropy. This control system can be used to control complex plants described by nonlinear, unstable, dissipative models. The control system is configured to use smart simulation techniques for controlling the shock absorber (plant).
In one embodiment, the control system comprises a learning system, such as a neural network that is trained by a genetic analyzer. The genetic analyzer uses a fitness function that maximizes sensor information while minimizing entropy production.
In one embodiment, a suspension control uses a difference between the time differential (derivative) of entropy from the learning control unit and the time differential of the entropy inside the controlled process (or a model of the controlled process) as a measure of control performance. In one embodiment, the entropy calculation is based on a thermodynamic model of an equation of motion for a controlled process plant that is treated as an open dynamic system.
The control system is trained by a genetic analyzer. The optimized control system provides an optimum control signal based on data obtained from one or more sensors. For example, in a suspension system, a plurality of angle and position sensors can be used. In an off-line (laboratory) learning mode, fuzzy rules are evolved using a kinetic model (or simulation) of a vehicle and is suspension system. Data from the kinetic model is provided to an entropy calculator which calculates input and output entropy production of the model. The input and output entropy productions are provided to a fitness function calculator that calculates a fitness function as a difference in entropy production rates for the genetic analyzer. The genetic analyzer uses the fitness function to develop a training signal for the off-line control system. Control parameters from the off-line control system are then provided to an online control system in the vehicle.
In one embodiment, the invention includes a method for controlling a nonlinear object (a plant) by obtaining an entropy production difference between a time differentiation (dSu/dt) of the entropy of the plant and a time differentiation (dSc/dt) of the entropy provided to the plant from a controller. A genetic algorithm that uses the entropy production difference as a fitness (performance) function evolves a control rule in an off-line controller. The nonlinear stability characteristics of the plant are evaluated using a Lyapunov function. The genetic analyzer minimizes entropy and maximizes sensor information content. Control rules from the off-line controller are provided to an online controller to control suspension system. In one embodiment, the online controller controls the damping factor of one or more shock absorbers (dampers) in the vehicle suspension system.
In some embodiments, the control method also includes evolving a control rule relative to a variable of the controller by means of a genetic algorithm. The genetic algorithm uses a fitness function based on a difference between a time differentiation of the entropy of the plant (dSu/dt) and a time differentiation (dSc/dt) of the entropy provided to the plant. The variable can be corrected by using the evolved control rule.
In another embodiment, the invention comprises an AI control system adapted to control a nonlinear plant. The AI control system includes a simulator configured to use a thermodynamic model of a nonlinear equation of motion for the plant. The thermodynamic model is based on a Lyapunov function (V), and the simulator uses the function V to analyze control for a state stability of the plant. The AI control system calculates an entropy production difference between a time differentiation of the entropy of said plant (dSu/dt) and a time differentiation (dSc/dt) of the entropy provided to the plant by a low-level controller that controls the plant. The entropy production difference is used by a genetic algorithm to obtain an adaptation function in which the entropy production difference is minimized. The genetic algorithm provides a teaching signal to a fuzzy logic classifier that determines a fuzzy rule by using a learning process. The fuzzy logic controller is also configured to form a control rule that sets a control variable of the controller in the vehicle.
In yet another embodiment, the invention comprises a new physical measure of control quality based on minimum production entropy and using this measure for a fitness function of genetic algorithm in optimal control system design. This method provides a local entropy feedback loop in the control system. The entropy feedback loop provides for optimal control structure design by relating stability of the plant (using a Lyapunov function) and controllability of the plant (based on production entropy of the control system). The control system is applicable to all control systems, including, for example, control systems for mechanical systems, bio-mechanical systems, robotics, electromechanical systems, etc.